A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

Associative learning theories assume that cue interaction and, specifically, retrospective revaluation occur only when the target cue is previously trained in compound with the to-be-revalued cue. However, there are recent demonstrations of retrospective revaluation in the absence of compound training (e.g., Matute & Pineño, 1998a, 1998b). Nevertheless, it seems reasonable to assume that cue interaction should be stronger when the cues are trained together than when they are trained apart. In two experiments with humans, we directly compared compound and elemental training of cues. The results showed that retrospective revaluation in the elemental condition can be as strong as and, sometimes, stronger than that in the compound condition. This suggests that within-compound associations are not necessary for retrospective revaluation to occur and that these effects can possibly be best understood in the framework of general interference theory. In the literature of animal conditioning and human associative learning, it is well known that if a cue, X, is consistently followed by an outcome, O (i.e., X–O), X is generally learned as a predictor of the occurrence of the outcome. It is also well known that responding to X in a subsequent test phase becomes altered if another cue, A, is trained in compound with X as a predictor of the same outcome. Some classic instances of these cue interaction effects in the animal learning literature are overshadowing (Pavlov, 1927), blocking (Kamin, 1968), conditioned inhibition (Pavlov, 1927), and the relative stimulus validity

The concordance between performance and judgements of the causal effectiveness of an instrumental action suggests that such actions are mediated by causal knowledge. Although causal learning exhibits many associative phenomena—blocking, inhibitory or preventative learning, and super-learning—judgements of the causal status of a cue can be changed retrospectively as a result of learning episodes that do not directly involve the cue. In order to explain retrospective revaluation, a modi®ed associative theory is described in which the learning processes for retrieved cue representations are the opposite to those for presented cues, and this theory is evaluated by studies of the role of within-compound associations in retrospective revaluation and blocking. However, this modi®ed theory only applies when the within-compound association represents a contiguous rather than a causal cue relationship. Causal learning and representation is a fundamental form of cognition, if not the fundamental form. Without the capacity to learn about and represent the causal relationships between our actions and their consequences, the mind would be radically disconnected from the world. However detailed and rich our knowledge, however sophisticated and complex our inferences and planning, cognition would be impotent if our thoughts could not be

Within-compound associations in retrospective revaluation and in direct learning: A challenge for comparator theory

Backward blocking (BB) and interference between cues (IbC) are cue competition effects produced by very similar manipulations. In a standard BB design both effects might occur simultaneously, which implies a potential problem to study BB. In the present study with humans, the magnitude of both effects was compared using a non causal scenario and a within subjects design. Previous studies have made this comparison using learning tasks framed within causal scenarios. This posits a limit to generalizing their findings to non-causal learning situations because there is ample evidence showing that participants engage in causal reasoning when tasks are causally framed. The results obtained showed BB and IbC effects of the same magnitude in a non causal framed task. This highlights the methodological need for an IbC control in BB experiments. Backward Blocking 3

Backward blocking (BB) and interference between cues (IbC) are cue competition effects produced by very similar manipulations. In a standard BB design, both effects might occur simultaneously, which implies a potential problem for studying BB. In the present study with humans, the magnitude of both effects was compared using a non-causal scenario and a within-subjects design. Previous studies have made this comparison using learning tasks framed within causal scenarios. This posits a limit to generalizing their findings to non-causal learning situations because there is ample evidence showing that participants engage in causal reasoning when tasks are causally framed. The results obtained showed BB and IbC effects of the same magnitude in a non– causally-framed task. This highlights the methodological need for an IbC control in BB experiments.